I’m a consultant and seminar leader who specializes in the graphical display of data. I train employees of corporations and organizations on effective data visualization. I also review documents and presentations for clients, suggesting improvements or alternative presentations as appropriate. I’m the author of Creating More Effective Graphs, published by Chart House in 2013 (reprinted from Wiley 2005). In addition to my one and two day seminars on creating more effective graphs, I offer short programs such as “Recognizing Misleading and Deceptive Graphs” and “How to Avoid Common Graphical Mistakes.” I received a Ph.D. in mathematical statistics from Columbia University, M.A. from Cornell University, and A.B. from Bryn Mawr College. I had a long career at Bell Laboratories before forming NBR, my consulting practice.

What's Wrong with this Graph?

This is the first post in a new blog designed to help you recognize misleading and deceptive graphs as well as become familiar with some common graphical mistakes. How many problems can you identify in this company valuation visual? I’ll return to the graph after I briefly introduce myself.

I’m Naomi Robbins – author of Creating More Effective Graphs(Wiley 2005). Jon Bruner, an editor at Forbes, invited me to write this blog after a presentation I gave on graphical mistakes at the Strata Conference in New York. In addition to pointing out errors in published graphs, I’ll also explain how to choose the right graph form to make your point when you’re creating your own graphs. Despite the fact that graphs are now ubiquitous in virtually every field of business, very few people have received any training on how to read or design a graph. I will share with you my favorite graph forms, the ones that communicate data in the most informative way. I’ll also discuss my favorite books and blogs on communicating data clearly. You’ll learn more about all of these topics in the coming weeks and months.

Although I may occasionally mention software, my approach will mainly be software agnostic. That is, the principles I emphasize apply no matter what software you use, just as the rules of grammar apply no matter what word processor you use.

Let’s return now to the figure. Did you notice that the bars start at $55 (billion) rather than zero? This may seem like a minor point, but in fact it’s a critical factor in how we perceive the information in a bar graph. We judge the value of the bars in a bar graph by their lengths. We cannot do a very good job if we’re only shown parts of the bars. More specifically, the second bar appears to be twice as high as the first bar, suggesting a doubling of valuation from December to January. but a closer look shows that the difference between the two is much less drastic: an increase from $60 billion to $66.2 billion. The takeaway: every bar graph needs a zero on its scale. If you see one without a zero, be careful. In a future post I’ll discuss whether line graphs and other graphs also need a zero on their scales.

Now let’s look at the horizontal axis. The bars are evenly spaced but the dates they represent are not. There is one bar for December, one for January, none for February, two for March, and so on. Therefore, the trend that results from following the top of the bars is distorted. In particular, the high valuation of $84 billion appears to hold for a lengthy period of time, when in fact the total time period at this value was less than a month (June 22 to July 19). Evenly spaced bars or tick marks for uneven intervals is a common problem. Whether the distortion was intentional or not, the reader receives misleading information.

The last thing you may have noticed about this graph is the excessive use of dollar signs on the vertical axis as well as in the data labels. All told, we are reminded that the data represent dollars twenty times (once in the label for the vertical axis, eight times with the tick mark labels and eleven times with the data labels on the bars.) Another unnecessary feature is the decimal point and zero on each tick mark label. Both contribute to a growing trend that I call “chart clutter.” It is not as serious a problem as the two issues discussed above but it does make the numbers themselves more difficult to decipher.

Do you see anything else wrong with the figure? Have you seen other examples of “chart clutter”? Use the comments to share your thoughts. In addition, I welcome any questions on charts and graphs for inclusion in future blog posts.

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As the exact amounts are given for each bar, the gridlines are somewhat superfluous. One could even think about dropping the vertical axis. It is not really needed here. By the way, how would you tackle the issue of the unevenly spaced time points?

My next post, “What’s Wrong with this Graph 2: Redoing the Facebook Valuation Graph” will show by example how to handle the unevenly spaced time points. I plan to publish it on Tuesday.

Good point! The data labels and gridlines are not both needed. I prefer keeping the axis/gridlines. My main reason follows: Even if most graph designers want to show their data accurately, there are some who want their results to appear better than they actually are. I have seen many graphs in corporate annual reports and other places with data labels but no axes. I am shocked by the number of times I’ve taken out my ruler, measured, and found that the graph was not drawn to scale. This is easier to do without axes. Another problem with data labels will be mentioned in Tuesday’s post.